Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

Summary Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Funding Bill & Melinda Gates Foundation.


Literature Review and Search Strings
To build the joint exposure-response curve we began with a literature review of each of the four sources of particulate matter exposure-ambient air pollution (AAP), household air pollution, (HAP), and secondhand smoke (SHS)-and diabetes. In stage 1, we conducted a PubMed search for the most recent meta-analysis or systematic review for each of the four sources. In stage 2, we found additional studies through searches of the literature or collaborators' knowledge of published and unpublished work. The search strategy and resulting studies are featured in Supplementary Figure S1.

Household Air Pollution Exposure
Exposure to household air pollution from solid fuels (HAP) has two fundamental components. The first is estimating the proportion of households using solid cooking fuels. We will call this the Proportion Model. The definition of solid fuel in our analysis includes coal, wood, charcoal, dung, and agricultural residues. The second component of the modeling involves estimating the exposure level of PM2.5 corresponding to solid-fuel use and measured in µg/m 3 for a given location and year. We will call that the PM2.5 Mapping Model.

Input data
Data estimating the percentage of individuals using solid cooking fuels came from international survey series such as Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS) and country-specific series such as the South Africa General Household Survey. We also included additional HAP estimates from the WHO Energy Database 6 .

Modelling strategy
Because we do not have data for all locations and years, we used a common GBD modelling tool to fill in the gaps. We modeled this data at the individual level using a three-step modelling strategy that uses linear regression, spatiotemporal regression, and Gaussian Process Regression (ST-GPR). The ST-GPR modelling process is detailed in Supplementary Appendix 1 of the GBD 2017 Risk Factor Capstone paper 3 , Section 2.3.3, pages 28-33. The first step is a mixed-effect linear regression of logit-transformed proportion of households using solid cooking fuels. The linear model includes fixed effects on maternal education and the proportion of the population living in urban areas and nested random effects on GBD region and GBD super-region with the following model formula:

PM2.5 Mapping Model
Not all who use solid fuel for cooking experience the same level of PM2.5 exposure. The PM2.5 mapping model estimates the level of exposure in µg/m 3 associated with using solid fuel in a given location and year. We use the WHO Global database of household energy measurements 7 and create our model based upon previously published work 8 . The database used for modeling in GBD 2017 contains about 90 studies from 41 unique GBD locations in 19 countries. In these studies researchers measured PM2.5 levels among individuals who used various fuel types for cooking with personal or kitchen monitors. Because these measurements capture PM2.5 exposure from both ambient and household sources, we first subtracted off the GBD estimated ambient exposure level for the given location and year to get the "excess" PM2.5 due to solid fuel use.
We ran a model with the following formula, where PM2.5 is the excess PM2.5 exposure (study measurement -ambient exposure level), sdi is the socio-demographic index for the given study location and year, and monitor_loc, measure_std, and non_solid are binary indicators of whether the monitoring was in the kitchen or using a personal monitor, whether the measurement took place over at least 48 hours, and whether the household used solid or non-solid fuels respectively: log (=>2.5) ~ /!8 + &2)8#2(_*2-+ &'"/,('_/#! + )2)_/2*8! We included the monitor_loc and the measure_std variables to account for systematic biases in study design and assumed a gold standard of personal monitoring and measurements over at least 48 hours. Non_solid is included because some studies took measurements of households using solid fuel and households using electricity or gas. We predicted out for all GBD locations and years based on sdi.
Finally, due to traditional gender roles and time spent indoors, studies show that women and children experience higher exposure levels due to household air pollution than men. To account for this in the model, we used seven studies, which reported personal measurements separately for men, women, and children, to generate ratios. These ratios were used to scale the PM2.5 mapping model accordingly. The scaled estimates were used in the proportional PAF calculations to determine the exposure level to obtain the RR from the IER.

Theoretical minimum-risk exposure level
The TMREL of the IER is estimated as a uniform distribution between 2.4 and 5.9 ug/m 3 and is based on observed exposures in several North American cohorts. This is described in more detail in Supplementary Appendix 1 of the GBD 2017 Risk Factor Capstone paper 3 , Section 4.4, page 73.

Exposure assessment/conversion in smoking studies
AS exposure is measured in categories of cigarettes-per-day such as 5-10 cigarettes-per-day. We converted this exposure to a PM2.5 value by taking the midpoint of the range and multiplying it by a conversion factor of 667 microgram/m 3 per cigarette, according to previous work and based on the following assumptions: average breathing rate of 18 m^3/day and inhaled dose of 12,000 micrograms PM2.5 mass per cigarette 9 .
We use two sources to estimate the PM2.5 exposure level for a given SHS study. The first is estimated number of cigarettes smoked per smoker per day for every location and year, 1980 through 2017. Secondly, a study in Sweden measured the PM2.5 exposure in homes of smokers 10 . We divided the household PM2.5 exposure level by the average number of cigarettes smoked per smoker per day in Sweden over the study duration to estimate the SHS PM2.5 exposure per cigarette (2.31 (95% U.I. 1.53, 3.39). For each of the seven SHS and Diabetes studies, we multiplied the estimated number of cigarettes per smoker per day by the average PM2.5 exposures per cigarette to generate a predicted PM2.5 exposure level.

Proportional PAF calculations
Let 0 ! be the proportion of the population exposed only to Ambient pollution and 0 " be the proportion of the population exposed to both ambient air pollution and household air pollution from solid fuel for cooking such that p ! + 0 " = 1.
Let D ! be the mean exposure of ambient PM2.5, D " be the additiClosedonal PM2.5 exposure due to solidfuel use, and D #$ be the counterfactual level of PM2.5 exposure defined by the TMREL. The average population exposure, denoted D %&'( , is a weighted average of exposures calculated as D ! + (p " * D " ).
The risk function is described elsewhere and is denoted as FGH(D) which is equal to the RR at a given exposure level, D.
Let HH ! = FGH(D ! ) be the RR for those only exposed to ambient particulate matter pollution; let HH " = FGH(D ! + D " ) be the RR for those exposed to both ambient and household sources of particulate matter; and let HH )* be a summary RR of all particulate matter exposure in the given population. We calculate HH )* to be a weighted average of the source specific RRs as follows, HH )* = 0 ! * HH ! + 0 " * HH " .
Let =IJ ! and =IJ " be the population attributable fractions for ambient and household sources respectively, and let =IJ )* be the PAF for all particulate matter exposure in the population.
A PAF for exposure level D is calculated with the following formula, =IJ + = ,, ! -,, "# ,, ! . Where HH + is the RR at exposure level D, and HH #$ is the RR at the counterfactual level of exposure or TMREL. In this case,

Followup
To et al.